What Are the Real Risks with AI?
AI offers powerful capabilities, but it also introduces risks that need to be understood and managed. AI systems learn patterns from data, not meaning or truth, which means they can behave in unpredictable or harmful ways if these risks are not addressed. By understanding these challenges, we can design safer, more trustworthy AI systems that support people rather than confuse or mislead them. In this section, we introduce the most common risks in modern AI development and explain why they matter in real-world applications.Hallucinations
What it means: A hallucination occurs when an AI generates incorrect, fabricated, or misleading information while sounding confident. Why it matters: Hallucinations can spread false information and cause users to rely on inaccurate outputs, especially in contexts involving education, leadership, counseling, or faith. How it shows up in Gloo: Gloo reduces hallucinations by grounding every answer in an organization’s verified content through the Data Engine. Instead of relying on model memory or guesses, Gloo retrieves real documents and uses them as the authoritative source for generated responses. The current state of AI means that its not currently impossible to reduce Hallucinations to zero. However, Gloo is actively working to make sure those Hallucinations are effectively eliminated specifically when it comes to Safety, Biblical Accuracy, and Theological Alignment.Bias
What it means: Bias occurs when an AI reflects unfair patterns, stereotypes, or imbalanced representation found in its training data. Why it matters: If not monitored, AI systems can unintentionally reinforce harmful assumptions or favor certain groups over others. How it shows up in Gloo: Gloo uses curated organizational content and constantly improving guardrails to minimize the influence of broad internet training data. By restricting responses to an organization’s own materials and theological boundaries, Gloo reduces unwanted bias in partner applications and experiences.Toxicity
What it means: Toxicity refers to outputs that are rude, offensive, harmful, or inappropriate. Why it matters: Toxic or harmful content can make AI unsafe in community environments and may violate organizational or platform guidelines. How it shows up in Gloo: Gloo applies safety filters, system prompts, and response shaping to prevent toxic or harmful outputs. Since responses are often grounded in approved content, the risk of inappropriate language or tone is significantly reduced.Model Collapse
What it means: Model collapse happens when models begin training on data generated by other models instead of human-created data, causing quality to degrade over time. Why it matters: Over-reliance on AI-generated data reduces originality, accuracy, and reliability in future models. How it shows up in Gloo: Gloo doesn’t use user content to retrain or fine tune the underlying models. This prevents AI generated material from re entering the training loop.Data Poisoning
What it means: Data poisoning occurs when harmful or misleading data is intentionally inserted into training sets to manipulate how the AI behaves. Why it matters: If poisoned data influences training, it can cause models to behave incorrectly, unpredictably, or in ways that are vulnerable to exploitation. How it shows up in Gloo: Because Gloo does not use just any uploaded content for model training, poisoned or adversarial data cannot alter model behavior. Content ingestion pipelines also include structural processing and enrichment steps that help identify malformed or suspicious inputs. Any training is done only at the express consent of the content owners and is highly curated by both the partners and Gloo toward a specific goal.Prompt Injection
What it means: Prompt injection is when a user manipulates the model through crafted input to override system instructions or cause unintended behavior. Why it matters: Prompt injection can lead to leaks of internal instructions, bypassing of guardrails, or harmful model actions. How it shows up in Gloo: Bad actors will always exist where technology meets humans. Gloo uses multiple layers of protection against prompt injection, including controlled system prompts, behavior guardrails, strict function calling, and content grounding. The model is prevented from executing unsafe instructions or overriding alignment rules. At Gloo these protections are always evolving and adapting to improve and address new attack vectors.Misalignment
What it means: Misalignment refers to a gap between what the AI is designed to do and what it actually does in real interactions. Why it matters: If a model’s behavior does not match organizational values or user intent, it can harm trust and produce outcomes that conflict with the mission or goals. How it shows up in Gloo: Gloo’s alignment system is constantly evolving to ensure responses match each organization’s theology, values, and content boundaries. The Data Engine, Studio’s rights and curation tools, and Chat for Teams system instructions work together to ensure the model behaves predictably and mission aligned.Next Up: What Types of AI Models Exist, and Why Do They Work Differently? In the next section, we will answer: “What distinguishes foundation models, frontier models, open-weight models, multilingual models, and instruction-tuned models, and when do you use each one?”